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Related Questions
- What are the common pitfalls that human annotators may overlook when creating training data for machine learning models, leading to biased outcomes?
- How do data quality issues, such as sampling bias and data imbalances, impact the fairness of machine learning models?
- What strategies can human annotators use to detect and mitigate biases in data, such as using fairness metrics and auditing techniques?
- How do human annotators balance the trade-off between model accuracy and fairness, and what are the consequences of prioritizing one over the other?
- What role do human annotators play in addressing issues of representativeness and diversity in training data, and how do they ensure that the data reflects the real-world population?
- Can human annotators use fairness-aware algorithms and techniques, such as debiasing and fairness regularization, to improve the fairness of machine learning models?
- What are some real-world examples of how human annotators have successfully achieved fairness and reduced bias in machine learning models, and what best practices can be learned from these examples?
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